How does Adbrew use Statistically Significant Impressions (SSI) and Statistically Significant Clicks (SSC) in automated bid adjustments?
Adbrew employs Statistically Significant Impressions (SSI) and Statistically Significant Clicks (SSC) as dynamic thresholds to optimize bids effectively. These parameters ensure that automated decisions are based on statistically significant data rather than arbitrary thresholds.
Overview of SSI and SSC Concepts
Statistically Significant Impressions (SSI) and Clicks (SSC) share a similar conceptual foundation:
Statistically Significant Clicks (SSC): Derived from the Conversion Rate (CVR), representing the minimum clicks expected to yield one order.
Statistically Significant Impressions (SSI): Derived from the Click-Through Rate (CTR), representing the minimum impressions expected to yield one click.
These metrics allow Adbrew to determine whether a target has accumulated sufficient impressions or clicks for reliable performance evaluation.
Practical Applications of SSI and SSC in Bid Rules
Adbrew integrates SSI and SSC into its bid automation rules to ensure dynamic and scalable threshold determination:
Using SSI for Impressions Analysis: SSI serves as a dynamic threshold for detecting targets that are not receiving enough impressions to reasonably expect a click. For example: - A bid increase rule can be triggered when recent impressions fall below a multiple of SSI, such as impressions < 2 × SSI. This approach prevents reliance on absolute impression counts and automatically adjusts to a target’s observed Click-Through Rate (CTR).
Transitioning Based on Data: Once a target consistently exceeds the SSI threshold and gathers sufficient clicks, the focus shifts to click-to-order metrics such as Conversion Rate (CVR), orders, or Advertising Cost of Sale (ACoS). This ensures optimization evolves with the target’s conversion performance.
Avoiding Hardcoded Values: By relying on SSI and SSC, Adbrew avoids the pitfalls of hardcoded impression numbers. Instead, thresholds scale dynamically based on observed historical data and CTR.
Multi-Level Fallback Mechanism for Data Selection
Adbrew employs a robust fallback mechanism when calculating SSC or SSI to ensure that the most reliable data is used:
Primary Source: The system starts with conversion or click-through rates directly tied to the advertised product data.
Fallback Levels: If product-specific data is insufficient, broader data sources are used sequentially, such as: - Product label level data - Account-level conversion rates - User-defined conversion rates
Adaptability: This multi-tiered fallback mechanism ensures adaptive decision-making based on available data, delivering consistent and statistically reliable outcomes for Automated Bid Management evaluations.
Conclusion
By integrating SSI and SSC into its bid rules and leveraging adaptable data fallback mechanisms, Adbrew facilitates precise and scalable bid optimizations. These statistically driven calculations help marketers make data-informed decisions, improve target performance, and transition seamlessly between impression and click metrics based on the target's behavior.